EdGwas

The goal of EdGwas is to help clustering outcome components (traits) that share some feature (genetic component) using polygenic risk scores (PRS).

Installation

You can install the development version from GitHub with:

# install.packages("devtools")
devtools::install_github("abuchardt/EdGwas")

Example

This is a basic example on simulated data:

library(EdGwas)
#> Registered S3 methods overwritten by 'ggplot2':
#>   method         from 
#>   [.quosures     rlang
#>   c.quosures     rlang
#>   print.quosures rlang
# Gaussian
N <- 100 #
q <- 9
p <- 500 #
set.seed(1)
X <- matrix(sample(0:2, N*p, replace=TRUE), nrow=N, ncol=p)
B <- matrix(0, nrow = p, ncol = q)
B[1:2, 1:5] <- 1
Y <- X %*% B + matrix(rnorm(N*q), nrow = N, ncol = q)

Run 5-fold cross-validation for edgwas

cvfit <- cv.edgwas(x = X, y = Y, nfolds = 5)
#> i:  1 , ....................
#> i:  2 , ....................
#> i:  3 , ....................
#> i:  4 , ....................
#> i:  5 , ....................

plot(cvfit, which = 1)

plot(cvfit, which = 3)